• Journal of The Korean Society of Grassland and Forage Science
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• v.32 no.3
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• pp.301-308
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• 2012

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages. This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical parameters of Italian ryegrass silages. A population of 267 Italian ryegrass silages representing a wide range in chemical parameters and fermentative characteristics was used in this investigation. Samples of silage were scanned at 2 nm intervals over the wavelength range 680~2,500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected on the basis of the highest coefficients of determination in cross validation ($R^2$) and the lowest standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical parameters with very high degree of accuracy. The $R^2$ and SECV were 0.98 (SECV 1.27%) for moisture, 0.88 (SECV 1.26%) for ADF, 0.84 (SECV 2.0%), 0.93 (SECV 0.96%) for CP and 0.78 (SECV 0.56), 0.81 (SECV 0.31%), 0.88 (SECV 1.26%) and 0.82 (SECV 4.46) for pH, lactic acid, TDN and RFV on a dry matter (%), respectively. Results of this experiment showed the possibility of NIRS method to predict the chemical composition and fermentation quality of Italian ryegrass silages as routine analysis method in feeding value evaluation and for farmer advice.

• Journal of Computing Science and Engineering
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• v.7 no.2
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• pp.132-138
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• 2013

Multi-modal techniques have received increasing interest in the neuroscientific and brain computer interface (BCI) communities in recent times. Two aspects of multi-modal imaging for BCI will be reviewed. First, the use of recordings of multiple subjects to help find subject-independent BCI classifiers is considered. Then, multi-modal neuroimaging methods involving combined electroencephalogram and near-infrared spectroscopy measurements are discussed, which can help achieve enhanced and robust BCI performance.

• Proceedings of the Korean Society of Crop Science Conference
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• 2001.05a
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• pp.84-85
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• 2001

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1251-1251
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• 2001

The routine analysis of milk chemical components is of major importance both for the management of animals in dairy farms and for quality control in dairy industries. NIRS technology is an analytical technique which greatly simplifies this routine. One of the most critical aspects in NIRS analysis of milk is sample preparation and analysis modes which should be fast and straightforward. An important difficulty when obtaining NIR spectra of milk is the high water content (80 to 90%) of this product, since water absorbs most of the infrared radiation, and, therefore, limits the accuracy of calibrating for other constituents. To avoid this problem, the DESIR system was set up. Other ways of radiation-sample interaction adapted for liquids or semi-liquids exist, which are practically instantaneous and with limited or null necessity of sample preparation: Transmission and Folded Transmission or Transflectance. The objective of the present work is to compare the precision and accuracy of milk calibration equations in two analysis modes: Reflectance (dry milk) and Folded Transmission (liquid milk). A FOSS-NIR Systems 6500 I spectrophotometer (400-2500 nm) provided with a spinning module was used. Two NIR spectroscopic methods for milk analysis were compared: a) folded transmission: liquid milk samples in a 0.1 pathlength sample cell (ref. IH-0345) and b) reflectance: dried milk samples in glass fibre filters placed in a standard ring cell. A set of 101 milk samples was used to develop the calibration equations, for the two NIR analysis modes, to predict casein, protein, fat and dry matter contents, and 48 milk samples to predict Somatic Cell Count (SCC). The calibrations obtained for protein, fat and dry matter have an excellent quantitative prediction power, since they present $r^2$ values higher than 0.9. The $r^2$ values are slightly lower for casein and SCC (0.88 and 0.89 respectively), but they still are sufficiently high. The accuracy of casein, protein and SCC equations is not affected by the analysis modes, since their ETVC values are very similar in reflectance and folded transmission (0.19% vs 0.21%; 0.16% vs 0.19% and 55.57% vs 53.11% respectively), Lower SECV values were obtained for the prediction of fat and dry matter with the folded transmission equations (0.14% and 0.25% respectively) compared to the results with the reflectance ones (0.43% and 0.34% respectively). In terms of accuracy and speed of analytical response, NIRS analysis of liquid milk is recommended (folded transmission), since the drying procedure takes 24 hours. However, both analysis modes offer satisfactory results.

• Journal of The Korean Society of Grassland and Forage Science
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• v.34 no.3
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• pp.214-218
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• 2014

This study was conducted to assess the feasibility of near-infrared reflectance spectroscopy (NIRS) as a rapid and reliable method for the estimation of crude protein (CP) fractions in forage legume mixtures (sudangrass and pea mixture, and kidney bean and potato mixture). A total of 178 samples were collected and their spectral reflectance obtained in the range of 400~2,500 nm. Of these, 50 samples were selected for calibration and validation, and 35 samples were used for calibration of the data set, and the modified partial least square regression (MPLSR) analysis was performed. The correlation coefficient ($r^2$) and the standard error of cross-validation (SECV) of the calibration models in the CP fractions, A, B1, B2, B3, and C, were 0.94 (1.05), 0.92 (0.74), 0.96 (0.95), 0.91 (0.42), and 0.83 (0.38), respectively. Fifteen samples were used for equation validation, and the $r^2$ and the standard error of prediction (SEP) were 0.87 (1.45), 0.91 (0.49), 0.94 (1.13), 0.36 (0.96), and 0.74 (0.67), respectively. This study showed that NIRS could be an effective tool for the rapid and precise estimation of CP fractions in forage legume mixtures.

• Proceedings of the PSK Conference
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• 2002.10a
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• pp.405.1-405.1
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• 2002

The amount of water for the cultivation of citrus is different based on the growing period. The water content in the leaves of citrus can be a index for watering during cultivation. The purpose of this study is to determine non-destructively the water content of Citrus leaves by using near infrared spectroscopy (NIRS). Citrus leaves were prepared from satsuma mandarin leaves (Citrus unshiu Marc. var. okitsu) ranging from 62.20 to 69.98% of water content by loss on drying, NIR reflectance spectra of Citrus leaves were acquired by using a fiber optic probe. (omitted)

• Proceedings of the Optical Society of Korea Conference
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• 2008.07a
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• pp.159-160
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• 2008

We measured hemodynamic responses of seizure in the mouse brain using frequencydomain near infrared spectroscopy (NIRS) and electroencephalogram (EEG). We adapted microfabricated optical holder for consistent contact of the optical fiber to the mouse brain. Our results show that the cerebral oxygenation and hemodynamics of mice can be stably monitored with EEG in the mouse brain.

• Journal of The Korean Society of Grassland and Forage Science
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• v.36 no.1
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• pp.50-57
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• 2016

This study was conducted to determine the effect of mathematical transformation on near infrared spectroscopy (NIRS) calibrations for the prediction of chemical composition and fermentation parameters in corn silage. Corn silage samples (n=407) were collected from cattle farms and feed companies in Korea between 2014 and 2015. Samples of silage were scanned at 1 nm intervals over the wavelength range of 680~2,500 nm. The optical data were recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with several spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation ($R^2{_{cv}}$) and the lowest standard error of cross validation (SECV). Results of this study revealed that the NIRS method could be used to predict chemical constituents accurately (correlation coefficient of cross validation, $R^2{_{cv}}$, ranging from 0.77 to 0.91). The best mathematical treatment for moisture and crude protein (CP) was first-order derivatives (1, 16, 16, and 1, 4, 4), whereas the best mathematical treatment for neutral detergent fiber (NDF) and acid detergent fiber (ADF) was 2, 16, 16. The calibration models for fermentation parameters had lower predictive accuracy than chemical constituents. However, pH and lactic acids were predicted with considerable accuracy ($R^2{_{cv}}$ 0.74 to 0.77). The best mathematical treatment for them was 1, 8, 8 and 2, 16, 16, respectively. Results of this experiment demonstrate that it is possible to use NIRS method to predict the chemical composition and fermentation quality of fresh corn silages as a routine analysis method for feeding value evaluation to give advice to farmers.

• Journal of agriculture & life science
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• v.46 no.2
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• pp.49-61
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• 2012

This study was carried out to investigate the chemical composition and the inorganic constituents of the green tea at the 3 picking periods (Ujeon, Sejag, Jungjag) in Hadong. The results as follows ; The contents of chlorophyll, tannin, vitamin-c and total catechin were increased as picking periods increased but the contents of total nitrogen, total free amino acids, theanine and caffeine were decreased on the reverse. The inorganic constituents Mg, Ca and Mn were increased as picking periods getting late but the Na, K, B contents were decreased on the reverse. The contents of the total nitrogen, chlorophyll, total free amino acid, theanine, caffeine and total catechin and Na, Mg, Ca, B and Se were insignificant differences between Ujeon and Sejag.

• Neonatal Medicine
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• v.28 no.1
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• pp.14-21
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• 2021

Purpose: Premature infants have immature respiratory control and cerebral autoregulation. We aimed to investigate changes in cerebral oxygenation during apnea with and without peripheral oxygen desaturation in premature infants. Methods: This prospective observational study was conducted at Inha University Hospital. Near-infrared spectroscopy (NIRS)-monitored regional cerebral oxygen saturation (rScO₂) and pulse oximeter-monitored peripheral oxygen saturation (SpO₂) were assessed during the first week of life in 16 stable, spontaneously breathing preterm infants. Apneic episodes that lasted for ≥20 seconds or were accompanied by desaturation or bradycardia were included for analysis. The average rScO₂ value during the 5-minute prior to apnea (baseline), the lowest rScO₂ value following apnea (nadir), the time to recover to baseline (recovery time), the area under the curve (AUC), and the overshoot above the baseline after recovery were analyzed. Results: The median gestational age and birth weight of the infants were 29.2 weeks (interquartile range [IQR], 28.5 to 30.5) and 1,130 g (IQR, 985 to 1,245), respectively. A total of 73 apneic episodes were recorded at a median postnatal age of 2 days (IQR, 1 to 4). The rScO₂ decreased significantly following apneic episodes regardless accompanied desaturation. There were no differences in baseline, nadir, or overshoot rScO₂ between the two groups. However, the rScO₂ AUC for apnea with desaturation was significantly higher than that for apnea without desaturation. Conclusion: Cerebral oxygenation can significantly decrease during apnea, especially when accompanied by reduced SpO₂. These results add the evidence for the clinical utility of NIRS in monitoring premature infants.